# Deep Learning for Detecting the Early Anatomical Effects of Alzheimer's Disease

> **NIH NIH R21** · MASSACHUSETTS GENERAL HOSPITAL · 2024 · $250,500

## Abstract

Project Summary
Longitudinal, within-subject approaches, have the potential to increase sensitivity and specificity, improving
the efficiency of clinical trials by requiring fewer subjects and providing potential surrogate endpoints to
assess therapeutic efficacy. There is also great potential that these tools will enable more sophisticated
anatomical modeling to better understand the temporal dynamics of progression. In Alzheimer’s Disease in
particular, early detection, prior to widespread and likely irreversible cell death, is crucial for the development
of effective therapeutic interventions. However, longitudinal tools have not yet been optimized for use in
clinical studies. Challenges include the reduction of noise across serial scans while providing each time point
equal relative weighting to avoid bias; adequately and appropriately accounting for atrophy; and handling
varying MRI contrast and distortion across time. In this proposal, we seek to improve longitudinal analysis in a
number of ways, leveraging the power of modern deep learning to increase accuracy, make it applicable to
any type of MRI contrast, radically reduce execution time, as well as make it usable in direct clinical
applications.
To achieve these aims we will employ newly developed image synthesis techniques to train networks to detect
small, “true” anatomical change hidden within a set of large-scale “MRI” distortions, that will capture
longitudinal differences in image acquisition such as gradient nonlinearities, field strength and B0 distortions,
and sequence parameter variations. The change-detection network will be cascaded with a deep registration
network that will learn to decompose the temporal warp into uninteresting MRI distortions and interesting
anatomical effects, then both warp fields and the aligned images will be provided to a segmentation network
to ensure no information is lost by the registration. The networks will learn to ignore MRI effects based on
their stereotypical behavior (e.g. the one-dimensionality of B0 distortions, the spatial smoothness of gradient
nonlinearities) and to detect the subtle anatomical changes such as increasing ventricular size or decreasing
hippocampal volume. The result will be a set of robust contrast-and-distortion-agnostic tools that highlight
potential disease effects for clinicians.

## Key facts

- **NIH application ID:** 10846724
- **Project number:** 5R21AG082082-02
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Bruce Fischl
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $250,500
- **Award type:** 5
- **Project period:** 2023-06-01 → 2026-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10846724

## Citation

> US National Institutes of Health, RePORTER application 10846724, Deep Learning for Detecting the Early Anatomical Effects of Alzheimer's Disease (5R21AG082082-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10846724. Licensed CC0.

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